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Record W2415343837 · doi:10.5603/ep.2014.0040

Ocena częstości występowania zespołu Wolframa w populacji dzieci z cukrzycą

2014· article· pl· W2415343837 on OpenAlexaff
Agnieszka Zmysłowska, Maciej Borowiec, Wojciech Fendler, Przemysława Jarosz‐Chobot, Małgorzata Myśliwiec, Agnieszka Szadkowska, Wojciech Młynarski

Bibliographic record

VenueEndokrynologia Polska · 2014
Typearticle
Languagepl
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsOptech (Canada)
FundersFundacja na rzecz Nauki Polskiej
KeywordsMedicineWolfram syndromePediatricsDiabetes mellitusConfidence intervalEpidemiologyPopulationInternal medicineEndocrinologyEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Wolfram syndrome (WFS) is the most frequent syndromic form of monogenic diabetes coexisting with optic atrophy and many other disorders. The aim of this study was to estimate the prevalence of Wolfram syndrome among children with diabetes in Poland. MATERIAL AND METHODS: These calculations were performed among Polish diabetic children, aged 0-18 years, from three administrative regions between January 2005 and December 2011. Epidemiological data was obtained by matching the results from the EURO-WABBPoland Project and the PolPeDiab Registry. RESULTS: Throughout the study period, we confirmed genetic diagnosis of Wolfram syndrome in 13 patients from Poland. Three patients originated from the studied regions with complete epidemiological data on paediatric diabetes. The total number of patients with diagnosed diabetes in the study equalled 2,568 cases. The prevalence of Wolfram syndrome among Polish children with diabetes is 0.12% (95% Confidence Interval 0.04-0.34%). CONCLUSIONS: We estimate that Wolfram syndrome is: 26 to 35 times less frequent than monogenic diabetes (MODY and neonatal diabetes) in the Polish paediatric population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.219
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2014
Admission routes1
Has abstractyes

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